2025
A Recipe for Causal Graph Regression: Confounding Effects Revisited
ICML 2025poster
Through recognizing causal subgraphs, causal graph learning (CGL) has risen to be a promising approach for improving the generalizability of graph neural networks under out-of-distribution (OOD) scenarios. However, the empirical successes of CGL techniques are mostly exemplified in classification se…